Bounded option combinations
A few options can produce many combinations. VariantOak brings product-option logic and its allocation limit into the same coding conversation, anchored to the files you select.
from itertools import product
from math import prod
def variants(groups, limit):
if type(limit) is not int or limit < 0:
raise ValueError("limit")
if not groups: return []
if any(len(set(g)) != len(g) for g in groups):
raise ValueError("duplicate option")
if prod(map(len, groups)) > limit:
raise ValueError("too many variants")
return list(product(*groups))Connection
Base URL, credentials and a reachable server.
Project
Test an empty group and duplicate option labels, rejecting excess combinations before allocating the output.
Review before applying
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
from itertools import product
from math import prod
def variants(groups, limit):
if type(limit) is not int or limit < 0:
raise ValueError("limit")
if not groups: return []
if any(len(set(g)) != len(g) for g in groups):
raise ValueError("duplicate option")
if prod(map(len, groups)) > limit:
raise ValueError("too many variants")
return list(product(*groups))Give catalog changes an explicit boundary
VariantOak provides a Windows coding workspace for examining product-option behavior before a small catalog change becomes a much larger operation. Select your project and describe the options the application accepts, the output it should produce and the point at which it should refuse the request. The AI conversation can then address the source files behind those decisions.
The option-combination example is a practical starting task. Adding another group can expand the result substantially, so ask the connected model to inspect where the implementation checks its limit. Discuss empty groups, duplicate labels and ordering as part of the same contract. This is work on your catalog code, not a built-in commerce integration or a promise that VariantOak manages product variants for you.
Review the proposed validation before the code that allocates the result. Run fixtures for accepted input and a request beyond the chosen limit, checking that rejection happens at the intended stage. If you prefer an unrestricted AI model, select one offered through a supported OpenAI-compatible or Anthropic endpoint and supply credentials when needed. Tools and context limits depend on the model server. The useful outcome is a bounded, explainable application change that you have checked against the catalog's actual rules.
Preparing a product-option change
Does VariantOak connect directly to my store?
No store integration is established by this workflow. Open the relevant source project and provide appropriate sanitized examples. The product-option scenario describes code you may develop and review, not a bundled catalog service or administrative connection.
Where should the combination limit be checked?
Inspect whether the implementation can determine the result size before constructing it. State the allowed limit in the task and test a request just beyond it, so rejection is verified before unnecessary output is allocated.
What should an empty option group mean?
That is a contract decision for your application. Explain whether it means no possible products or invalid input, then inspect the implementation and callers for agreement. Do not let an incidental library behavior define the product rule.
Why review ordering as well as the result count?
Callers may depend on stable option order for display or comparison. Include expected small outputs in your fixtures and check their sequence, especially when a proposed cleanup changes how the groups are iterated.
VariantOak · Updated 2026-10-25
